Access to robust, long-term biological monitoring data are essential for understanding global species trends and informing conservation and policy efforts. However, the availability of data is often taxonomically and spatially biased, leaving large “ignorance gaps" in open access biodiversity records. This study investigated the global data gaps for Orthoptera (grasshoppers, crickets, katydids and relatives), as a group that is both culturally and agriculturally significant across the world. The authors hypothesized that data completeness for the group would be richer in the northern hemisphere in temperate regions and comparatively sparser in tropical regions in the Global South.
Researchers compiled global Orthoptera occurrence data from 23 repositories including GBIF, creating a comprehensive database of almost 30 thousand extant species. They then calculated five data quality indices—taxonomic, survey and temporal completeness, and survey and temporal evenness. The indices were then combined to create a composite “ignorance map” to identify biodiversity knowledge gaps for the group.
Findings were consistent with the authors’ hypothesis that data was more available, complete and of higher quality in North America and Central Europe, while data was taxonomically and spatially more restricted in tropical regions and most of Asia. They noted that higher taxonomic and survey completeness in data-rich areas was not always accompanied by strong survey and temporal evenness. Instead, many areas suffered from limited taxonomic refinement, inventory integrity, limited diversity of years sampled and an imbalance in the number of records among known species and years.
Ignorance gaps for this taxon also likely reflect disparities in overall access to taxonomists, data digitization mechanisms and funding across different regions. The authors noted that the methods used in this study to map ignorance gaps could be transferrable to asses other taxonomic groups, as a mechanism to better understand and inspire action to address data deficiencies more broadly.